DexNDM bridges the sim-to-real gap for stable in-hand rotation of complex objects, enabling learning from biased real-world data without requiring any successful demonstrations.

Proposes learning multi-modal trajectory policies to improve data efficiency in robotic manipulation tasks.

A zero-shot workflow reasoning approach for agentic control of embodied manipulation, enabling robots to perform complex tasks without task-specific training.

A coupled egocentric control approach for whole-body robot teleoperation that coordinates body and limb movements from an egocentric perspective, submitted to Humanoids 2026.

A Mixture-of-Transformers robot policy that achieves 85 ms inference on RTX 4090 by separating visual dynamics from action generation. Demonstrates efficient real-time robot policy execution through architectural decomposition.

A scalable vision-language-action (VLA) foundation model pretrained on over 100k hours of real-world manipulation trajectories, enabling out-of-the-box mobile manipulation capabilities.

Proposes training local policies in simulation that transfer zero-shot to real-world long-horizon robotic manipulation tasks. Addresses the challenge of generalizing learned behaviors across extended task sequences.

Trains perception-driven dexterous manipulation policies in simulation for zero-shot real-world transfer on humanoid robots. Focuses on bridging the sim-to-real gap for vision-based dexterous tasks.

A framework enabling humanoid robots to execute diverse motions within physical limits, with video demonstrations and open-source code. Targets generalizable motion tracking across varied humanoid morphologies.

A pipeline combining active vision and high-resolution tactile sensing for dexterous manipulation on high-DoF robot hands, achieving approximately 2.5 minutes of continuous autonomous control on real-world tasks such as food preparation.